1 Introduction

The analyses reported in this document are part of the SARTools.DESeq2.Kallisto.Transcripts.batchSex.byGroup project. The aim is to find features that are differentially expressed between Control, Combination, Cort and Pregnanedione. The statistical analysis process includes data normalization, graphical exploration of raw and normalized data, test for differential expression for each feature between the conditions, raw p-value adjustment and export of lists of features having a significant differential expression between the conditions. In this analysis, the Sex effect will be taken into account in the statistical models.

The analysis is performed using the R software [1], Bioconductor [2] packages including DESeq2 [3,4] and the SARTools package developed at PF2 - Institut Pasteur. Normalization and differential analysis are carried out according to the DESeq2 model and package. This report comes with additional tab-delimited text files that contain lists of differentially expressed features.

For more details about the DESeq2 methodology, please refer to its related publications [3,4].

2 Description of raw data

The count data files and associated biological conditions are listed in the following table.

Table 1: Data files and associated biological conditions.
label	file	Treatment	Egg_mass	Group	Sex
A11_Mem_12	A11-Mem-12_S35/abundance.tsv	Control	65.56	A	M
A12_Mem_13	A12-Mem-13_S38/abundance.tsv	Control	65.27	B	F
A7_Mem_7	A7-Mem-7_S23/abundance.tsv	Control	63.21	A	M
B11_Mem_27	B11-Mem-27_S36/abundance.tsv	Control	70.29	B	F
B5_Mem_19	B5-Mem-19_S18/abundance.tsv	Control	69.21	A	M
B7_Mem_21	B7-Mem-21_S24/abundance.tsv	Control	66.20	B	F
C10_Mem_42	C10-Mem-42_S34/abundance.tsv	Control	67.15	B	F
C2_Mem_30	C2-Mem-30_S7/abundance.tsv	Control	72.07	B	F
C4_Mem_32	C4-Mem-32_S15/abundance.tsv	Control	61.62	B	F
C9_Mem_40	C9-Mem-40_S31/abundance.tsv	Control	63.83	B	F
A10_Mem_10	A10-Mem-10_S32/abundance.tsv	Combination	62.43	A	M
B9_Mem_24	B9-Mem-24_S30/abundance.tsv	Combination	64.66	B	F
C1_Mem_29	C1-Mem-29_S3/abundance.tsv	Combination	65.52	B	F
C11_Mem_44	C11-Mem-44_S37/abundance.tsv	Combination	68.94	A	M
C7_Mem_36	C7-Mem-36_S25/abundance.tsv	Combination	73.03	B	F
C8_Mem_38	C8-Mem-38_S28/abundance.tsv	Combination	63.55	A	M
D1_Mem_46	D1-Mem-46_S4/abundance.tsv	Combination	66.06	B	F
D2_Mem_47	D2-Mem-47_S8/abundance.tsv	Combination	67.75	A	M
D3_Mem_49	D3-Mem-49_S12/abundance.tsv	Combination	71.84	A	M
D4_Mem_54	D4-Mem-54_S16/abundance.tsv	Combination	70.20	B	F
A3_Mem_3	A3-Mem-3_S9/abundance.tsv	Cort	64.25	A	M
B1_Mem_14	B1-Mem-14_S2/abundance.tsv	Cort	62.92	A	M
B10_Mem_25	B10-Mem-25_S33/abundance.tsv	Cort	69.28	B	F
B2_Mem_15	B2-Mem-15_S6/abundance.tsv	Cort	60.19	B	F
B3_Mem_16	B3-Mem-16_S10/abundance.tsv	Cort	70.99	A	M
B4_Mem_18	B4-Mem-18_S14/abundance.tsv	Cort	68.25	B	F
B8_Mem_22	B8-Mem-22_S27/abundance.tsv	Cort	67.17	A	M
C12_Mem_45	C12-Mem-45_S40/abundance.tsv	Cort	64.93	B	F
C3_Mem_31	C3-Mem-31_S11/abundance.tsv	Cort	65.32	B	F
C6_Mem_34	C6-Mem-34_S22/abundance.tsv	Cort	72.14	B	F
A1_Mem_1	A1-Mem-1_S1/abundance.tsv	Pregnanedione	61.11	B	F
A2_Mem_2	A2-Mem-2_S5/abundance.tsv	Pregnanedione	72.16	A	M
A4_Mem_4	A4-Mem-4_S13/abundance.tsv	Pregnanedione	65.38	A	M
A5_Mem_5	A5-Mem-5_S17/abundance.tsv	Pregnanedione	65.01	B	F
A6_Mem_6	A6-Mem-6_S20/abundance.tsv	Pregnanedione	66.62	A	M
A8_Mem_8	A8-Mem-8_S26/abundance.tsv	Pregnanedione	65.80	B	F
A9_Mem_9	A9-Mem-9_S29/abundance.tsv	Pregnanedione	69.29	B	F
B12_Mem_28	B12-Mem-28_S39/abundance.tsv	Pregnanedione	68.55	B	F
B6_Mem_20	B6-Mem-20_S21/abundance.tsv	Pregnanedione	67.41	B	F
C5_Mem_33	C5-Mem-33_S19/abundance.tsv	Pregnanedione	71.40	B	F

After loading the data we first have a look at the raw data table itself. The data table contains one row per annotated feature and one column per sequenced sample. Row names of this table are feature IDs (unique identifiers). The table contains raw count values representing the number of reads that map onto the features. For this project, there are 28756 features in the count data table.

Table 2: Partial view of the count data table.
	A11_Mem_12	A12_Mem_13	A7_Mem_7	B11_Mem_27	B5_Mem_19	B7_Mem_21	C10_Mem_42	C2_Mem_30	C4_Mem_32	C9_Mem_40	A10_Mem_10	B9_Mem_24	C1_Mem_29	C11_Mem_44	C7_Mem_36	C8_Mem_38	D1_Mem_46	D2_Mem_47	D3_Mem_49	D4_Mem_54	A3_Mem_3	B1_Mem_14	B10_Mem_25	B2_Mem_15	B3_Mem_16	B4_Mem_18	B8_Mem_22	C12_Mem_45	C3_Mem_31	C6_Mem_34	A1_Mem_1	A2_Mem_2	A4_Mem_4	A5_Mem_5	A6_Mem_6	A8_Mem_8	A9_Mem_9	B12_Mem_28	B6_Mem_20	C5_Mem_33
ENSGALT00000000003.5	2	0	2	5	10	3	5	5	2	4	2	4	12	5	3	3	4	2	5	2	3	3	4	4	2	3	4	2	4	5	5	5	8	9	7	4	4	2	7	10
ENSGALT00000000012.7	55	61	58	55	99	79	55	67	68	79	57	60	81	57	78	75	74	52	55	72	70	69	60	76	51	70	75	67	72	89	74	56	70	50	110	56	45	63	87	66
ENSGALT00000000022.6	250	187	196	222	304	243	250	273	253	242	235	231	257	193	258	283	243	258	228	246	243	305	285	260	246	291	254	216	222	260	259	246	224	241	277	271	199	200	318	250
ENSGALT00000000051.6	0	2	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	1	0
ENSGALT00000000059.7	2	0	6	3	2	2	3	3	3	4	0	3	0	1	2	1	6	2	3	2	5	7	2	2	0	1	3	3	2	6	4	1	3	0	2	1	4	1	0	0
ENSGALT00000000063.6	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0

Looking at the summary of the count table provides a basic description of these raw counts (min and max values, median, etc).

Table 3: Summary of the raw counts.
	A11_Mem_12	A12_Mem_13	A7_Mem_7	B11_Mem_27	B5_Mem_19	B7_Mem_21	C10_Mem_42	C2_Mem_30	C4_Mem_32	C9_Mem_40	A10_Mem_10	B9_Mem_24	C1_Mem_29	C11_Mem_44	C7_Mem_36	C8_Mem_38	D1_Mem_46	D2_Mem_47	D3_Mem_49	D4_Mem_54	A3_Mem_3	B1_Mem_14	B10_Mem_25	B2_Mem_15	B3_Mem_16	B4_Mem_18	B8_Mem_22	C12_Mem_45	C3_Mem_31	C6_Mem_34	A1_Mem_1	A2_Mem_2	A4_Mem_4	A5_Mem_5	A6_Mem_6	A8_Mem_8	A9_Mem_9	B12_Mem_28	B6_Mem_20	C5_Mem_33
Min.	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
1st Qu.	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
Median	1	1	1	1	2	1	1	2	2	1	1	1	1	1	1	2	1	1	1	1	2	1	2	2	2	2	2	1	1	2	1	1	1	1	2	2	1	1	3	2
Mean	111	99	106	86	137	102	111	122	124	114	101	117	113	94	99	116	119	113	112	116	127	121	123	125	114	127	119	104	104	117	104	114	108	116	123	117	94	112	123	122
3rd Qu.	34	33	32	29	45	33	36	40	39	37	33	37	35	30	33	39	33	33	34	37	40	36	39	40	39	42	41	34	33	39	35	33	35	38	43	40	31	34	46	39
Max.	124047	83649	91338	92056	91339	79913	80451	120146	90819	74708	66927	80931	116360	140311	63500	78507	290793	259297	162132	99781	99023	179870	122544	105604	68318	76310	72692	71539	121737	79967	98702	121839	74589	80157	103847	71497	63809	95654	75971	92866

Figure 1 shows the total number of mapped and counted reads for each sample. We expect total read counts to be similar within conditions, they may be different across conditions. Total counts sometimes vary widely between replicates. This may happen for several reasons, including:

different rRNA contamination levels between samples (even between biological replicates);
slight differences between library concentrations, since they may be difficult to measure with high precision.

Figure 1: Number of mapped reads per sample. Colors refer to the biological condition of the sample.

Figure 2 shows the percentage of features with no read count in each sample. We expect this percentage to be similar within conditions. Features with null read counts in the 40 samples are left in the data but are not taken into account for the analysis with DESeq2. Here, 4956 features (17.23%) are in this situation (dashed line). Results for those features (fold-change and p-values) are set to NA in the results files.

Figure 2: Percentage of features with null read counts in each sample.

Figure 3 shows the distribution of read counts for each sample. For sake of readability, \(\text{log}_2(\text{counts}+1)\) are used instead of raw counts. Again we expect replicates to have similar distributions. In addition, this figure shows if read counts are preferably low, medium or high. This depends on the organisms as well as the biological conditions under consideration.

Figure 3: Density distribution of read counts.

It may happen that one or a few features capture a high proportion of reads (up to 20% or more). This phenomenon should not influence the normalization process. The DESeq2 normalization has proved to be robust to this situation [Dillies, 2012]. Anyway, we expect these high count features to be the same across replicates. They are not necessarily the same across conditions. Figure 4 and table 4 illustrate the possible presence of such high count features in the data set.

Figure 4: Percentage of reads associated with the sequence having the highest count (provided in each box on the graph) for each sample.

Table 4: Percentage of reads associated with the sequences having the highest counts.
	A11_Mem_12	A12_Mem_13	A7_Mem_7	B11_Mem_27	B5_Mem_19	B7_Mem_21	C10_Mem_42	C2_Mem_30	C4_Mem_32	C9_Mem_40	A10_Mem_10	B9_Mem_24	C1_Mem_29	C11_Mem_44	C7_Mem_36	C8_Mem_38	D1_Mem_46	D2_Mem_47	D3_Mem_49	D4_Mem_54	A3_Mem_3	B1_Mem_14	B10_Mem_25	B2_Mem_15	B3_Mem_16	B4_Mem_18	B8_Mem_22	C12_Mem_45	C3_Mem_31	C6_Mem_34	A1_Mem_1	A2_Mem_2	A4_Mem_4	A5_Mem_5	A6_Mem_6	A8_Mem_8	A9_Mem_9	B12_Mem_28	B6_Mem_20	C5_Mem_33
ENSGALT00000012060.6	3.89	2.94	2.98	0.82	1.43	2.73	2.53	1.21	2.33	2.14	2.31	2.39	2.33	1.41	2.24	2.35	2.06	2.01	1.36	1.47	1.79	2.24	1.49	1.70	1.19	2.08	2.12	1.99	1.75	2.38	1.78	3.71	2.41	2.22	2.93	1.54	2.22	2.10	2.15	2.64
ENSGALT00000028027.6	3.11	2.25	2.10	0.62	1.19	2.13	1.94	0.96	1.84	1.54	1.87	1.85	1.81	1.05	1.83	1.93	1.74	1.69	1.17	1.10	1.45	1.87	1.16	1.39	1.04	1.76	1.65	1.60	1.50	2.02	1.45	3.18	1.96	1.74	2.44	1.24	1.76	1.72	1.69	2.31
ENSGALT00000068173.2	2.83	2.13	1.84	0.58	1.09	2.03	1.76	0.93	1.78	1.45	1.64	1.68	1.75	1.05	1.61	1.81	1.65	1.72	1.07	1.05	1.39	1.83	1.04	1.27	0.92	1.55	1.51	1.52	1.38	1.80	1.41	2.97	1.76	1.61	2.19	1.17	1.68	1.60	1.50	2.08
ENSGALT00000024434.5	2.50	1.99	2.92	1.65	2.32	2.10	1.76	1.69	2.04	1.88	2.25	2.40	1.22	1.74	1.29	2.13	1.89	1.78	2.68	2.18	2.71	2.32	1.52	1.92	2.01	1.66	1.40	2.06	1.48	2.08	1.47	2.74	1.95	2.40	1.91	2.12	2.37	2.98	1.21	2.38
ENSGALT00000103112.1	1.76	1.22	1.07	3.74	1.54	0.93	2.52	3.42	2.54	2.27	1.51	1.97	3.57	5.21	1.51	2.28	8.50	8.01	5.03	3.00	1.69	5.19	3.47	2.94	2.08	2.03	1.57	2.40	4.07	1.75	3.31	2.83	1.37	1.23	1.16	1.21	1.43	2.53	1.12	2.11
ENSGALT00000040832.4	1.51	1.20	1.11	1.62	1.41	0.95	2.12	2.06	1.83	1.80	1.34	1.66	1.54	2.87	1.33	1.67	2.39	2.49	2.46	2.01	1.32	1.71	1.91	1.75	1.56	1.70	1.36	1.70	2.33	1.46	1.29	1.76	1.33	1.11	1.31	1.11	1.24	1.59	1.08	1.67
ENSGALT00000101725.1	0.93	0.72	0.71	0.99	0.97	0.59	1.42	1.51	1.32	1.25	1.02	1.13	0.82	1.79	0.93	1.08	1.23	1.39	1.60	1.50	0.88	0.86	1.26	1.13	1.20	1.09	0.94	1.16	1.58	0.97	0.70	0.98	0.89	0.73	0.84	0.74	0.82	1.01	0.78	1.00

We may wish to assess the similarity between samples across conditions. A pairwise scatter plot is produced (figure 5) to show how replicates and samples from different biological conditions are similar or different (\(\text{log}_2(\text{counts}+1)\) are used instead of raw count values). Moreover, as the Pearson correlation has been shown not to be relevant to measure the similarity between replicates, the SERE statistic has been proposed as a similarity index between RNA-Seq samples [5]. It measures whether the variability between samples is random Poisson variability or higher. Pairwise SERE values are printed in the lower triangle of the pairwise scatter plot. The value of the SERE statistic is:

0 when samples are identical (no variability at all: this may happen in the case of a sample duplication);
1 for technical replicates (technical variability follows a Poisson distribution);
greater than 1 for biological replicates and samples from different biological conditions (biological variability is higher than technical one, data are over-dispersed with respect to Poisson). The higher the SERE value, the lower the similarity. It is expected to be lower between biological replicates than between samples of different biological conditions. Hence, the SERE statistic can be used to detect inversions between samples.

Figure 5: Pairwise comparison of samples (not produced when more than 30 samples).

3 Variability within the experiment: data exploration

The main variability within the experiment is expected to come from biological differences between the samples. This can be checked in two ways. The first one is to perform a hierarchical clustering of the whole sample set. This is performed after a transformation of the count data which can be either a Variance Stabilizing Transformation (VST) or a regularized log transformation (rlog) [3,4].

A VST is a transformation of the data that makes them homoscedastic, meaning that the variance is then independent of the mean. It is performed in two steps: (i) a mean-variance relationship is estimated from the data with the same function that is used to normalize count data and (ii) from this relationship, a transformation of the data is performed in order to get a dataset in which the variance is independent of the mean. The homoscedasticity is a prerequisite for the use of some data analysis methods, such as hierarchical clustering or Principal Component Analysis (PCA). The regularized log transformation is based on a GLM (Generalized Linear Model) on the counts and has the same goal as a VST but is more robust in the case when the size factors vary widely.

Figure 6 shows the dendrogram obtained from VST-transformed data. An euclidean distance is computed between samples, and the dendrogram is built upon the Ward criterion. We expect this dendrogram to group replicates and separate biological conditions.

Figure 6: Sample clustering based on normalized data.

Another way of visualizing the experiment variability is to look at the first principal components of the PCA, as shown on the figure 7. On this figure, the first principal component (PC1) is expected to separate samples from the different biological conditions, meaning that the biological variability is the main source of variance in the data.

Figure 7: First two components of a Principal Component Analysis, with percentages of variance associated with each axis.

For the statistical analysis, we need to take into account the effect of the Sex parameter. Statistical models and tests will thus be adjusted on it.

4 Normalization

Normalization aims at correcting systematic technical biases in the data, in order to make read counts comparable across samples. The normalization proposed by DESeq2 relies on the hypothesis that most features are not differentially expressed. It computes a scaling factor for each sample. Normalized read counts are obtained by dividing raw read counts by the scaling factor associated with the sample they belong to. Scaling factors around 1 mean (almost) no normalization is performed. Scaling factors lower than 1 will produce normalized counts higher than raw ones, and the other way around. Two options are available to compute scaling factors: locfunc=“median” (default) or locfunc=“shorth”. Here, the normalization was performed with locfunc=“median”.

Table 5: Normalization factors.
	A11_Mem_12	A12_Mem_13	A7_Mem_7	B11_Mem_27	B5_Mem_19	B7_Mem_21	C10_Mem_42	C2_Mem_30	C4_Mem_32	C9_Mem_40	A10_Mem_10	B9_Mem_24	C1_Mem_29	C11_Mem_44	C7_Mem_36	C8_Mem_38	D1_Mem_46	D2_Mem_47	D3_Mem_49	D4_Mem_54	A3_Mem_3	B1_Mem_14	B10_Mem_25	B2_Mem_15	B3_Mem_16	B4_Mem_18	B8_Mem_22	C12_Mem_45	C3_Mem_31	C6_Mem_34	A1_Mem_1	A2_Mem_2	A4_Mem_4	A5_Mem_5	A6_Mem_6	A8_Mem_8	A9_Mem_9	B12_Mem_28	B6_Mem_20	C5_Mem_33
Size factor	0.94	0.90	0.90	0.79	1.26	0.94	0.99	1.12	1.09	1.05	0.90	1.02	1.04	0.82	0.94	1.06	0.95	0.91	0.94	1.04	1.13	1.02	1.13	1.12	1.08	1.19	1.15	0.94	0.91	1.08	0.98	0.92	0.97	1.06	1.16	1.12	0.86	0.95	1.24	1.09

The histograms (figure 8) can help to validate the choice of the normalization parameter (“median” or “shorth”). Under the hypothesis that most features are not differentially expressed, each size factor represented by a red line is expected to be close to the mode of the distribution of the counts divided by their geometric means across samples.

Figure 8: Diagnostic of the estimation of the size factors.

The figure 9 shows that the scaling factors of DESeq2 and the total count normalization factors may not perform similarly.

Figure 9: Plot of the estimated size factors and the total number of reads per sample.

Boxplots are often used as a qualitative measure of the quality of the normalization process, as they show how distributions are globally affected during this process. We expect normalization to stabilize distributions across samples. Figure 10 shows boxplots of raw (left) and normalized (right) data respectively.

Figure 10: Boxplots of raw (left) and normalized (right) read counts.

5 Differential analysis

5.1 Modelisation

DESeq2 aims at fitting one linear model per feature. For this project, the design used is counts ~ Sex + Treatment and the goal is to estimate the models’ coefficients which can be interpreted as \(\log_2(\texttt{FC})\). These coefficients will then be tested to get p-values and adjusted p-values.

5.2 Outlier detection

Model outliers are features for which at least one sample seems unrelated to the experimental or study design. For every feature and for every sample, the Cook’s distance [6] reflects how the sample matches the model. A large value of the Cook’s distance indicates an outlier count and p-values are not computed for the corresponding feature.

5.3 Dispersions estimation

The DESeq2 model assumes that the count data follow a negative binomial distribution which is a robust alternative to the Poisson law when data are over-dispersed (the variance is higher than the mean). The first step of the statistical procedure is to estimate the dispersion of the data. Its purpose is to determine the shape of the mean-variance relationship. The default is to apply a GLM (Generalized Linear Model) based method (fitType=“parametric”), which can handle complex designs but may not converge in some cases. The alternative is to use fitType=“local” as described in the original paper [3] or fitType=“mean”. The parameter used for this project is fitType=“parametric”. Then, DESeq2 imposes a Cox Reid-adjusted profile likelihood maximization [7] and uses the maximum a posteriori (MAP) of the dispersion [Wu, 2013].

Figure 11: Dispersion estimates (left) and diagnostic of log-normality (right).

The left panel on figure 11 shows the result of the dispersion estimation step. The x- and y-axes represent the mean count value and the estimated dispersion respectively. Black dots represent empirical dispersion estimates for each feature (from the observed counts). The red dots show the mean-variance relationship function (fitted dispersion value) as estimated by the model. The blue dots are the final estimates from the maximum a posteriori and are used to perform the statistical test. Blue circles (if any) point out dispersion outliers. These are features with a very high empirical variance (computed from observed counts). These high dispersion values fall far from the model estimation. For these features, the statistical test is based on the empirical variance in order to be more conservative than with the MAP dispersion. These features will have low chance to be declared significant. The figure on the right panel allows to check the hypothesis of log-normality of the dispersions.

5.4 Statistical test for differential expression

Once the dispersion estimation and the model fitting have been done, DESeq2 can perform the statistical testing. Figure 12 shows the distributions of raw p-values computed by the statistical test for the comparison(s) done. This distribution is expected to be a mixture of a uniform distribution on \([0,1]\) and a peak around 0 corresponding to the differentially expressed features.

Figure 12: Distribution(s) of raw p-values.

5.5 Independent filtering

DESeq2 can perform an independent filtering to increase the detection power of differentially expressed features at the same experiment-wide type I error. Since features with very low counts are not likely to see significant differences typically due to high dispersion, it defines a threshold on the mean of the normalized counts irrespective of the biological condition. This procedure is independent because the information about the variables in the design formula is not used [4].

Table 6 reports the thresholds used for each comparison and the number of features discarded by the independent filtering. Adjusted p-values of discarded features are then set to NA.

Table 6: Number of features discarded by the independent filtering for each comparison.
Test vs Ref	Threshold	# discarded
Combination vs Control	0.02	4956
Cort vs Control	0.03	5869
Pregnanedione vs Control	0.02	5430
Cort vs Combination	0.02	4956
Pregnanedione vs Combination	0.02	5430
Pregnanedione vs Cort	0.02	4956

5.6 Final results

A p-value adjustment is performed to take into account multiple testing and control the false positive rate to a chosen level \(\alpha\). For this analysis, a BH p-value adjustment was performed [8] and the level of controlled false positive rate was set to 0.05.

Table 7: Number of up-, down- and total number of differentially expressed features for each comparison.
Test vs Ref	# down	# up	# total
Combination vs Control	22	17	39
Cort vs Control	23	26	49
Pregnanedione vs Control	28	10	38
Cort vs Combination	10	17	27
Pregnanedione vs Combination	46	54	100
Pregnanedione vs Cort	61	79	140

Figure 13 represents the MA-plot of the data for the comparisons done, where differentially expressed features are highlighted in red. A MA-plot represents the log ratio of differential expression as a function of the mean intensity for each feature. Triangles correspond to features having a too low/high \(\log_2(\text{FC})\) to be displayed on the plot.

Figure 13: MA-plot(s) of each comparison. Red dots represent significantly differentially expressed features.

Figure 14 shows the volcano plots for the comparisons performed and differentially expressed features are still highlighted in red. A volcano plot represents the log of the adjusted P value as a function of the log ratio of differential expression.

Figure 14: Volcano plot(s) of each comparison. Red dots represent significantly differentially expressed features.

Full results as well as lists of differentially expressed features are provided in the following text files which can be easily read in a spreadsheet. For each comparison:

TestVsRef.complete.txt contains results for all the features;
TestVsRef.up.txt contains results for significantly up-regulated features. Features are ordered from the most significant adjusted p-value to the less significant one;
TestVsRef.down.txt contains results for significantly down-regulated features. Features are ordered from the most significant adjusted p-value to the less significant one.

These files contain the following columns:

Id: unique feature identifier;
sampleName: raw counts per sample;
norm.sampleName: rounded normalized counts per sample;
baseMean: base mean over all samples;
Control, Combination, Cort and Pregnanedione: means (rounded) of normalized counts of the biological conditions;
FoldChange: fold change of expression, calculated as \(2^{\log_2(\text{FC})}\);
log2FoldChange: \(\log_2(\text{FC})\) as estimated by the GLM model. It reflects the differential expression between Test and Ref and can be interpreted as \(\log_2(\frac{\text{Test}}{\text{Ref}})\). If this value is:
- around 0: the feature expression is similar in both conditions;
- positive: the feature is up-regulated (\(\text{Test} > \text{Ref}\));
- negative: the feature is down-regulated (\(\text{Test} < \text{Ref}\));
stat: Wald statistic for the coefficient tested;
pvalue: raw p-value from the statistical test;
padj: adjusted p-value on which the cut-off \(\alpha\) is applied;
dispGeneEst: dispersion parameter estimated from feature counts (i.e. black dots on figure 11);
dispFit: dispersion parameter estimated from the model (i.e. red dots on figure 11);
dispMAP: dispersion parameter estimated from the Maximum A Posteriori model;
dispersion: final dispersion parameter used to perform the test (i.e. blue dots and circles on figure 11);
betaConv: convergence of the coefficients of the model (TRUE or FALSE);
maxCooks: maximum Cook’s distance of the feature.

6 R session information and parameters

The versions of the R software and Bioconductor packages used for this analysis are listed below. It is important to save them if one wants to re-perform the analysis in the same conditions.

R version 3.5.1 (2018-07-02), x86_64-apple-darwin15.6.0
Locale: en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
Running under: macOS High Sierra 10.13.6
Matrix products: default
BLAS: /System/Library/Frameworks/Accelerate.framework/Versions/A/Frameworks/vecLib.framework/Versions/A/libBLAS.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/3.5/Resources/lib/libRlapack.dylib
Base packages: base, datasets, graphics, grDevices, methods, parallel, stats, stats4, utils
Other packages: Biobase 2.42.0, BiocGenerics 0.28.0, BiocParallel 1.16.6, DelayedArray 0.8.0, DESeq2 1.22.2, dplyr 0.8.3, edgeR 3.24.3, forcats 0.4.0, genefilter 1.64.0, GenomeInfoDb 1.18.2, GenomicRanges 1.34.0, ggplot2 3.2.0, IRanges 2.16.0, limma 3.38.3, matrixStats 0.54.0, purrr 0.3.2, readr 1.3.1, readxl 1.3.1, S4Vectors 0.20.1, SARTools 1.6.6, stringr 1.4.0, SummarizedExperiment 1.12.0, tibble 2.1.1, tidyr 0.8.3, tidyverse 1.2.1, xtable 1.8-4
Loaded via a namespace (and not attached): acepack 1.4.1, annotate 1.60.1, AnnotationDbi 1.44.0, assertthat 0.2.1, backports 1.1.4, base64enc 0.1-3, bit 1.1-14, bit64 0.9-7, bitops 1.0-6, blob 1.2.0, broom 0.5.2, cellranger 1.1.0, checkmate 1.9.4, cli 1.1.0, cluster 2.1.0, colorspace 1.4-1, compiler 3.5.1, crayon 1.3.4, data.table 1.12.2, DBI 1.0.0, digest 0.6.20, evaluate 0.14, foreign 0.8-71, Formula 1.2-3, geneplotter 1.60.0, generics 0.0.2, GenomeInfoDbData 1.2.0, glue 1.3.1, grid 3.5.1, gridExtra 2.3, gtable 0.3.0, haven 2.1.1, Hmisc 4.2-0, hms 0.5.0, htmlTable 1.13.1, htmltools 0.3.6, htmlwidgets 1.3, httr 1.4.0, jsonlite 1.6, knitr 1.24, lattice 0.20-38, latticeExtra 0.6-28, lazyeval 0.2.2, locfit 1.5-9.1, lubridate 1.7.4, magrittr 1.5, Matrix 1.2-17, memoise 1.1.0, modelr 0.1.4, munsell 0.5.0, nlme 3.1-140, nnet 7.3-12, packrat 0.5.0, pillar 1.4.2, pkgconfig 2.0.2, R6 2.4.0, RColorBrewer 1.1-2, Rcpp 1.0.2, RCurl 1.95-4.12, rlang 0.4.0, rmarkdown 1.14, rpart 4.1-15, rsconnect 0.8.15, RSQLite 2.1.2, rstudioapi 0.10, rvest 0.3.4, scales 1.0.0, splines 3.5.1, stringi 1.4.3, survival 2.44-1.1, tidyselect 0.2.5, tools 3.5.1, vctrs 0.2.0, withr 2.1.2, xfun 0.8, XML 3.98-1.19, xml2 1.2.0, XVector 0.22.0, yaml 2.2.0, zeallot 0.1.0, zlibbioc 1.28.0

Parameter values used for this analysis are:

workDir: /Users/kfield/Documents/chickeneggs/SARTools.DESeq2.Kallisto.Transcripts.batchSex.byGroup
projectName: SARTools.DESeq2.Kallisto.Transcripts.batchSex.byGroup
author: Ken Field
targetFile: ../sex.eggs.target.txt
rawDir: ../kallisto/
featuresToRemove: NULL
varInt: Treatment
condRef: Control
batch: Sex
fitType: parametric
cooksCutoff: TRUE
independentFiltering: TRUE
alpha: 0.05
pAdjustMethod: BH
typeTrans: VST
locfunc: median
colors: dodgerblue, firebrick1, MediumVioletRed, SpringGreen

Bibliography

1. R Core Team. R: A language and environment for statistical computing. Vienna, Austria : R Foundation for Statistical Computing, 2017 :

2. Gentleman RC, Carey VJ, Bates DM et al. Bioconductor: Open software development for computational biology and bioinformatics. Genome Biology 2004 ; 5: R80.

3. Anders S, Huber W. Differential expression analysis for sequence count data. Genome Biology 2010 ; 11: R106.

4. Love MI, Huber W, Anders S. Moderated estimation of fold change and dispersion for rna-seq data with deseq2. Genome Biology 2014 ; 15: 550.

5. Schulze SK, Kanwar R, Gölzenleuchter M et al. SERE: Single-parameter quality control and sample comparison for rna-seq. BMC Genomics 2012 ; 13: 524.

6. Cook RD. Detection of influential observation in linear regression. Technometrics 1977 ; 19: 15–18.

7. Cox DR, Reid N. Parameter orthogonality and approximate conditional inference. Journal of the Royal Statistical Society. Series B (Methodological) 1987 ; 49: 1–39.

8. Benjamini Y, Hochberg Y. Controlling the false discovery rate: A practical and powerful approach to multiple testing. Journal of the Royal Statistical Society. Series B (Methodological) 1995 ; 57: 289–300.

Statistical report of project SARTools.DESeq2.Kallisto.Transcripts.batchSex.byGroup: pairwise comparison(s) of conditions with DESeq2

Ken Field

2019-08-24